Covid19 Data
1 Introduction
Team A are the following members: Amal Alqahtani, Peng Jiaxiang, Elahi Naureen, and Mu Xinya. You may find our work over on GitHub.
Coronavirus disease-19 (COVID-19) has spread rapidly around the world, and many risk factors have been hypothesized to affect case and death rates. We felt that a relevant discussion to have would be What are the most regions with the highest number of deaths? What can we say about patient demographics? Does race considered a significant risk factor for increased Covid19 incidence in the United States?’ Have there been any general trends amongst the health conditions? These questions are all suited to EDA, and with these questions in mind, we want to see if we could find data on Covid19 that would be readily available for us to analyze. Eventually, our question morphed into the following: What are the factors (i.e. patient demographics, social determinants of health, environmental variables, underlying health conditions, country of origin) affecting COVID-19 numbers of cases and death rate among different geographical locations in US?
We were able to find a dataset called Covid-19-Dataset on Github over here: https://github.com/johndurbin93/Covid-19-Dataset. This dataset includes COVID-19 confirmed case number and death number through April 14, 2020 which were obtained for each U.S. county from the Center for Systems Science and Engineering (CSSE) Coronavirus Resource Center at Johns Hopkins University. Race demographics for counties was obtained from the County Health Rankings and Roadmaps Program database. Daily temperature data for counties was obtained from the National Oceanic and Atmospheric Administration. This data was compiled by a group of reserchers.
The report is organized as follows:
- Summary of Dataset
- Description of Data
- What are the patient demographics? (demographics Data)
- Where do …… come from? or What are the most regions with the highest number of deaths? (country of origin Data)
- What are the social determinants of health? (social determinants of health Data)
- Environmental variables Data Goes Here?
- Are there any common underlying health conditions? (health conditions Data)
- Have there been any ……. trends among patient over the times? (Trends Over Time)
- Are patient a certain race? (race Data)
- Discussion And Conclusion
2 Summary of Dataset
The data looks like the following:
Province State State Code Latitude
Longitude Tests Days Since 1st Case Total cases (4/14/2020)
Deaths (4/14/2020) Population (for demographic %'s)
% less than 18 years of age % 65 and over % Black
% American Indian & Alaska Native % Asian
% Native Hawaiian/Other Pacific Islander % Hispanic % Non-Hispanic White
% Not Proficient in English % Female % Rural
Population Density per Square mile of Land (2010)
Housing Density Per Square Mile of Land
Avg Daily March 2011 Sunlight (KJ/m²) Missing HI and AK GDP 2018
GDP/capita Percentage Living in Poverty, All Ages, 2016
Air Quality, Annual Average Ambient Concentrations of PM2.5, 2014
Primary Care Physicians Ratio Dentist Ratio Mental Health Provider Ratio
High School Graduation Rate % Some College % Unemployed
% Children in Poverty Income Inequality Ratio (80th%/20th%)
% Single-Parent Households Social Association Rate Violent Crime Rate
Air pollution: Average Daily PM2.5 Presence of Drinking Water Violation
% Severe Housing Problems Housing: Severe Cost Burden Housing: Overcrowding
Housing: Inadequate Facilities % Drive Alone to Work
% Long Commute - Drives Alone Sleep <7 Hours_Percent Sleep <7 Hours_CI_Low
Sleep <7 Hours_CI_High Diabetes Total Percentage Diabetes Male Percentage
Diabetes Female Percentage
Coronary Heart Disease Death Rate per 100,000, All Ages, All Races/Ethnicities, Both Genders, 2014-2016
Hypertension Death Rate per 100,000 (any mention), 35+, All Races/Ethnicities, Both Genders, 2014-2016
Obesity, Age-Adjusted Percentage, 20+. 2015 % Fair or Poor Health
Average Number of Physically Unhealthy Days
Average Number of Mentally Unhealthy Days % Low Birthweight
% Smokers (adults) % Adults with Obesity Food Environment Index
% Physically Inactive % With Access to Exercise Opportunities
% Excessive Drinking % Uninsured
Preventable Hospitalization Rate (Preventable hospital stays)
% With Annual Mammogram % Flu Vaccinated
Chronic Respiratory Disease: mortality rate per 100K (2014)
Liver Disease: crude mortality rate per 100K (1999-2018)
Liver Disease: % of Total Deaths (1999-2018)
Liver Disease: crude mortality rate per 100K (2018)
Liver Disease: % of Total Deaths (2018) Avg Temp Peak Growth-10 Rate
Avg Temp 10 Before First-Current Avg Temp First-Current
First Case Stay At Home No Cases
No Stay At Home Order Stay At Home Order After First Case
[ reached getOption("max.print") -- omitted 7 rows ]tibble [3,144 × 83] (S3: tbl_df/tbl/data.frame)
$ Province : chr [1:3144] "Autauga" "Baldwin" "Barbour" "Bibb" ...
$ State : chr [1:3144] "Alabama" "Alabama" "Alabama" "Alabama" ...
$ State Code : chr [1:3144] "AL" "AL" "AL" "AL" ...
$ Latitude : num [1:3144] 32.5 30.7 31.9 33 34 ...
$ Longitude : num [1:3144] -86.6 -87.7 -85.4 -87.1 -86.6 ...
$ Tests : num [1:3144] 33835 33835 33835 33835 33835 ...
$ Days Since 1st Case : num [1:3144] 22 31 12 16 21 20 21 28 27 21 ...
$ Total cases (4/14/2020) : num [1:3144] 23 87 11 17 16 8 8 62 216 9 ...
$ Deaths (4/14/2020) : num [1:3144] 1 2 0 0 0 0 0 0 10 0 ...
$ Population (for demographic %'s) : num [1:3144] 55601 218022 24881 22400 57840 ...
$ % less than 18 years of age : num [1:3144] 23.7 21.6 20.9 20.5 23.2 ...
$ % 65 and over : num [1:3144] 15.6 20.4 19.4 16.5 18.2 ...
$ % Black : num [1:3144] 19.34 8.78 48.03 21.12 1.46 ...
$ % American Indian & Alaska Native : num [1:3144] 0.48 0.772 0.659 0.438 0.654 ...
$ % Asian : num [1:3144] 1.225 1.15 0.454 0.237 0.32 ...
$ % Native Hawaiian/Other Pacific Islander : num [1:3144] 0.112 0.067 0.185 0.116 0.121 ...
$ % Hispanic : num [1:3144] 2.97 4.65 4.28 2.62 9.57 ...
$ % Non-Hispanic White : num [1:3144] 74.3 83.1 45.6 74.6 86.9 ...
$ % Not Proficient in English : num [1:3144] 0.82 0.544 1.632 0.268 1.725 ...
$ % Female : num [1:3144] 51.4 51.5 47.2 46.8 50.7 ...
$ % Rural : chr [1:3144] "42.002162300000002" "42.279099100000003" "67.789634699999993" "68.352607500000005" ...
$ Population Density per Square mile of Land (2010) : num [1:3144] 91.8 114.6 31 36.8 88.9 ...
$ Housing Density Per Square Mile of Land : num [1:3144] 37.2 65.5 13.4 14.4 37 7.2 12.8 88 28.5 29.4 ...
$ Avg Daily March 2011 Sunlight (KJ/m²) Missing HI and AK : num [1:3144] 18450 18855 18611 18235 17239 ...
$ GDP 2018 : num [1:3144] 1483414 5774289 787425 364197 849114 ...
$ GDP/capita : num [1:3144] NA NA NA NA NA NA NA NA NA NA ...
$ Percentage Living in Poverty, All Ages, 2016 : chr [1:3144] "13.5" "11.7" "29.9" "20.100000000000001" ...
$ Air Quality, Annual Average Ambient Concentrations of PM2.5, 2014 : chr [1:3144] "11.7" "10.3" "11.5" "11.2" ...
$ Primary Care Physicians Ratio : chr [1:3144] "92.500694444444449" "57.167361111111113" "131.62569444444446" "85.875694444444449" ...
$ Dentist Ratio : chr [1:3144] "128.70902777777778" "84.125694444444449" "115.20902777777779" "186.66736111111109" ...
$ Mental Health Provider Ratio : chr [1:3144] "178.20902777777778" "43.250694444444441" "12441:1" "186.66736111111109" ...
$ High School Graduation Rate : chr [1:3144] "90" "86.361576799999995" "81.410256399999994" "83.763837600000002" ...
$ % Some College : num [1:3144] 62 67.4 34.9 44.1 53.4 ...
$ % Unemployed : chr [1:3144] "3.6290788599999999" "3.6153821599999998" "5.1713842100000003" "3.9718277299999998" ...
$ % Children in Poverty : chr [1:3144] "19.3" "13.9" "43.9" "27.8" ...
$ Income Inequality Ratio (80th%/20th%) : num [1:3144] 5.23 4.42 5.68 4.37 4.43 ...
$ % Single-Parent Households : chr [1:3144] "26.2426791" "24.139601500000001" "56.603425999999999" "28.689236099999999" ...
$ Social Association Rate : num [1:3144] 12.07 10.21 7.52 8.38 8.45 ...
$ Violent Crime Rate : chr [1:3144] "272.28222" "203.66039599999999" "414.27786099999997" "89.349125999999998" ...
$ Air pollution: Average Daily PM2.5 : chr [1:3144] "11.7" "10.3" "11.5" "11.2" ...
$ Presence of Drinking Water Violation : chr [1:3144] "No" "No" "No" "No" ...
$ % Severe Housing Problems : num [1:3144] 14.7 13.6 14.6 10.5 10.5 ...
$ Housing: Severe Cost Burden : num [1:3144] 12.83 12.28 13.45 9.98 7.85 ...
$ Housing: Overcrowding : num [1:3144] 1.202 1.271 1.689 0.255 1.891 ...
$ Housing: Inadequate Facilities : num [1:3144] 1.346 0.479 0.603 0.709 1.091 ...
$ % Drive Alone to Work : num [1:3144] 86.5 84.3 83.4 84.9 86.2 ...
$ % Long Commute - Drives Alone : num [1:3144] 39.6 41.7 32.2 49.8 59.4 46 32.3 31.2 31.8 46.1 ...
$ Sleep <7 Hours_Percent : num [1:3144] 35.9 33.3 38.6 38.1 35.9 ...
$ Sleep <7 Hours_CI_Low : num [1:3144] 35 32.5 37.7 37.1 34.8 ...
$ Sleep <7 Hours_CI_High : num [1:3144] 36.8 34.1 39.5 39.2 37.1 ...
$ Diabetes Total Percentage : num [1:3144] 9.9 8.5 15.7 13.3 14.9 22.4 16.9 15.6 17.5 12.2 ...
$ Diabetes Male Percentage : num [1:3144] 10.1 8.8 16.1 14.5 17 22.1 17.7 16.7 18.3 13.7 ...
$ Diabetes Female Percentage : num [1:3144] 9.8 8.4 15.4 12.2 12.9 22.6 16.4 14.7 16.7 10.7 ...
$ Coronary Heart Disease Death Rate per 100,000, All Ages, All Races/Ethnicities, Both Genders, 2014-2016: chr [1:3144] "112.7" "108" "63" "63.8" ...
$ Hypertension Death Rate per 100,000 (any mention), 35+, All Races/Ethnicities, Both Genders, 2014-2016 : chr [1:3144] "241.4" "171.5" "192.5" "142.19999999999999" ...
$ Obesity, Age-Adjusted Percentage, 20+. 2015 : chr [1:3144] "37.6" "31.3" "44.7" "37.9" ...
$ % Fair or Poor Health : num [1:3144] 20.9 17.5 29.6 19.4 21.7 ...
$ Average Number of Physically Unhealthy Days : num [1:3144] 4.74 4.22 5.43 4.59 4.86 ...
$ Average Number of Mentally Unhealthy Days : num [1:3144] 4.65 4.3 5.19 4.55 4.89 ...
$ % Low Birthweight : chr [1:3144] "8.6195286200000005" "8.3450031800000009" "11.474558699999999" "10.30871" ...
$ % Smokers (adults) : num [1:3144] 18.1 17.5 22 19.1 19.2 ...
$ % Adults with Obesity : num [1:3144] 33.3 31 41.7 37.6 33.8 37.2 43.3 38.5 40.1 35 ...
$ Food Environment Index : chr [1:3144] "7.2" "8" "5.6" "7.8" ...
$ % Physically Inactive : num [1:3144] 34.7 26.5 23.5 33.5 30.3 24.6 39.5 31.7 30.1 31.3 ...
$ % With Access to Exercise Opportunities : chr [1:3144] "69.130124100000003" "73.713549" "53.166769899999998" "16.251363699999999" ...
$ % Excessive Drinking : num [1:3144] 15 18 12.8 15.6 14.2 ...
$ % Uninsured : chr [1:3144] "8.7216859499999995" "11.3334045" "12.2427925" "10.206252599999999" ...
$ Preventable Hospitalization Rate (Preventable hospital stays) : chr [1:3144] "7108" "4041" "6209" "5961" ...
$ % With Annual Mammogram : chr [1:3144] "41" "43" "45" "40" ...
$ % Flu Vaccinated : chr [1:3144] "41" "44" "37" "38" ...
$ Chronic Respiratory Disease: mortality rate per 100K (2014) : num [1:3144] 81.8 54.3 69.8 84.5 87 ...
$ Liver Disease: crude mortality rate per 100K (1999-2018) : chr [1:3144] "15.805600800000001" "19.802747700000001" "12.9148204" "13.557851400000001" ...
$ Liver Disease: % of Total Deaths (1999-2018) : chr [1:3144] "1.9061999999999999E-4" "8.1862999999999999E-4" "8.2999999999999998E-5" "7.0199999999999999E-5" ...
$ Liver Disease: crude mortality rate per 100K (2018) : chr [1:3144] "Unreliable" "27.520158500000001" "NA" "NA" ...
$ Liver Disease: % of Total Deaths (2018) : chr [1:3144] "1.7882999999999999E-4" "1.073E-3" "NA" "NA" ...
$ Avg Temp Peak Growth-10 Rate : num [1:3144] 20.5 22.1 18.5 18.4 18.2 ...
$ Avg Temp 10 Before First-Current : num [1:3144] 19.9 20.9 18.1 17.7 17.8 ...
$ Avg Temp First-Current : num [1:3144] 19.3 21.8 17 16 17.5 ...
$ First Case : POSIXct[1:3144], format: "2020-03-24" "2020-03-15" ...
$ Stay At Home : POSIXct[1:3144], format: "2020-04-04" "2020-04-04" ...
$ No Cases : num [1:3144] 0 0 0 0 0 0 0 0 0 0 ...
$ No Stay At Home Order : num [1:3144] 0 0 0 0 0 0 0 0 0 0 ...
$ Stay At Home Order After First Case : num [1:3144] 1 1 1 1 1 1 1 1 1 1 ...The Covid19 data has 83 columns and 3144 rows/entries, for a total of 260952 individual data points. The variables are the following:
- Province
- State
- Tests
- Total cases
- Deaths
- Population (for demographic %’s)
- % less than 18 years of age
- % 65 and over
- % Black
- % American Indian & Alaska Native
- % Asian
- % Native Hawaiian/Other Pacific Islander
- % Hispanic
- % Non-Hispanic White
- % Not Proficient in English
- % Female
- % Rural
- Sleep <7 Hours_Percent
- Sleep <7 Hours_CI_Low
- Sleep <7 Hours_CI_High
- Diabetes Total Percentage
- Diabetes Male Percentage
- Diabetes Female Percentage
- Coronary Heart Disease Death Rate per 100,000, All Ages , All Races/Ethnicities, Both Genders, 2014-2016
- Hypertension Death Rate per 100,000 (any mention), 35+, All Races/Ethnicities, Both Genders, 2014-2016
- Obesity, Age-Adjusted Percentage, 20+. 2015
- % Fair or Poor Health
- Average Number of Physically Unhealthy Days
- Average Number of Mentally Unhealthy Days
- % Low Birthweight
- % Smokers (adults)
- % Adults with Obesity
- Food Environment Index
<<<<<<< Updated upstream
- % Physically Inactive
- % With Access to Exercise Opportunities
- % Excessive Drinking
- % Uninsured
- Preventable Hospitalization Rate (Preventable hospital stays)
- % With Annual Mammogram
- % Flu Vaccinated
- Chronic Respiratory Disease: mortality rate per 100K (2014)
- Liver Disease: crude mortality rate per 100K (1999-2018)
- Liver Disease: % of Total Deaths (1999-2018)
- Liver Disease: crude mortality rate per 100K (2018)
- Liver Disease: % of Total Deaths (2018)
- Avg Temp Peak Growth-10 Rate
- Avg Temp 10 Before First-Current
- Avg Temp First-Current
To prepare our data for EDA we dropped the ………: …….. NAs were also removed.
=======To prepare our data for EDA we clean the dataset and remove all NAs.
tibble [3,144 × 15] (S3: tbl_df/tbl/data.frame)
$ Province : chr [1:3144] "New York City" "Nassau" "Suffolk" "Westchester" ...
$ State : chr [1:3144] "New York" "New York" "New York" "New York" ...
$ total_cases : num [1:3144] 110465 25250 22691 20191 16323 ...
$ deaths : num [1:3144] 7905 1001 608 596 577 ...
$ Population (for demographic %'s) : chr [1:3144] "8623000" "1358343" "1481093" "967612" ...
$ % less than 18 years of age : chr [1:3144] "20.9" "21.459675499999999" "21.134324500000002" "21.900513799999999" ...
$ % 65 and over : chr [1:3144] "14.1" "17.763039200000001" "16.862951899999999" "17.053116299999999" ...
$ % Black : chr [1:3144] "24.3" "11.6331442" "7.3924459799999998" "13.8042935" ...
$ % American Indian & Alaska Native : chr [1:3144] "0.4" "0.54294092000000005" "0.61373593999999998" "0.95647842000000005" ...
$ % Asian : chr [1:3144] "13.9" "10.4504532" "4.1896086199999996" "6.43553408" ...
$ % Native Hawaiian/Other Pacific Islander: chr [1:3144] "0.1" "0.1" "9.5899999999999999E-2" "0.13228443000000001" ...
$ % Hispanic : chr [1:3144] "29.1" "17.231362000000001" "19.775260599999999" "25.140345499999999" ...
$ % Non-Hispanic White : chr [1:3144] "32.1" "59.333835399999998" "67.190378999999993" "53.088118000000001" ...
$ % Not Proficient in English : chr [1:3144] "9" "5.3660427200000003" "4.00639637" "6.3180527499999997" ...
$ % Female : chr [1:3144] "52.3" "51.306334300000003" "50.771693599999999" "51.559095999999997" ...3 Chapter 3: Independent Variables EDA
3.1 United States COVID-19 Cases and Deaths by Provinces (Cities)
3.1.1 What are the top 15 Provinces based on the number of cases?
The following bar chart shows the top 15 cities by number of Covid19 cases.
3.1.2 What are the top 15 Provinces based on the number of deaths?
The following bar chart shows the top 15 cities by number of deaths.
3.1.3 What is the average cases for each State?
State total_cases
1 Alabama 59.03
2 Alaska 9.83
3 Arizona 258.60
4 Arkansas 19.44
5 California 437.43
6 Colorado 122.41
7 Connecticut 1682.00
8 Delaware 638.33
9 District of Columbia 2058.00
10 Florida 323.07
11 Georgia 85.74
12 Hawaii 101.60
13 Idaho 33.32
14 Illinois 227.48
15 Indiana 94.12
16 Iowa 19.20
17 Kansas 13.84
18 Kentucky 17.32
19 Louisiana 335.34
20 Maine 45.88
21 Maryland 394.75
22 Massachusetts 1843.87
23 Michigan 316.96
24 Minnesota 19.10
25 Mississippi 37.68
26 Missouri 40.98
27 Montana 7.18
28 Nebraska 9.48
29 Nevada 184.35
30 New Hampshire 103.50
31 New Jersey 3196.29
32 New Mexico 40.82
33 New York 3274.52
34 North Carolina 51.20
35 North Dakota 6.45
36 Ohio 82.81
37 Oklahoma 28.51
[ reached 'max' / getOption("max.print") -- omitted 14 rows ]3.1.4 What is the average deaths for each State?
State deaths
1 Alabama 1.701
2 Alaska 0.172
3 Arizona 7.133
4 Arkansas 0.427
5 California 13.328
6 Colorado 5.109
7 Connecticut 83.375
8 Delaware 14.333
9 District of Columbia 67.000
10 Florida 7.836
11 Georgia 3.270
12 Hawaii 1.800
13 Idaho 0.750
14 Illinois 8.510
15 Indiana 4.207
16 Iowa 0.444
17 Kansas 0.657
18 Kentucky 0.900
19 Louisiana 15.922
20 Maine 1.250
21 Maryland 12.667
22 Massachusetts 49.600
23 Michigan 21.133
24 Minnesota 0.920
25 Mississippi 1.366
26 Missouri 1.284
27 Montana 0.143
28 Nebraska 0.161
29 Nevada 7.059
30 New Hampshire 0.300
31 New Jersey 133.476
32 New Mexico 0.939
33 New York 174.871
34 North Carolina 1.130
35 North Dakota 0.151
36 Ohio 3.705
37 Oklahoma 1.403
[ reached 'max' / getOption("max.print") -- omitted 14 rows ]3.1.5 Which cities had the greatest % of population of people with poor health?
3.2 Patient Demographics
3.2.1 What are the patient demographics?
| TC | Population | young | old | black | AIAN | Asian | NH | Hispanic | NHW | Female | Poverty | Social | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Min | 0 | 88 | 0.0 | 4.8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6 | 2.7 | 26.8 | 3.4 | 0.0 |
| Q1 | 2 | 11034 | 20.1 | 16.3 | 0.7 | 0.4 | 0.5 | 0.0 | 2.4 | 64.7 | 49.4 | 11.4 | 8.2 |
| Median | 9 | 25758 | 22.1 | 19.0 | 2.2 | 0.6 | 0.7 | 0.1 | 4.4 | 83.5 | 50.3 | 14.8 | 11.1 |
| Mean | 191 | 105871 | 22.1 | 19.3 | 8.8 | 2.4 | 1.5 | 0.1 | 9.6 | 76.2 | 49.9 | 15.9 | 11.6 |
| Q3 | 39 | 67013 | 23.8 | 21.8 | 9.6 | 1.3 | 1.4 | 0.1 | 9.9 | 92.3 | 51.0 | 19.0 | 14.4 |
| Max | 110465 | 10105518 | 42.0 | 57.6 | 85.4 | 92.5 | 43.4 | 48.9 | 96.4 | 97.9 | 56.9 | 48.6 | 52.3 |
From the average of the output results, we can see that the average proportion of teenagers under the age of 18 is 22.1%, and the average proportion of people over 65 is 19.3%. The largest number of all races is Non-Hispanic White, with an average proportion of 76.2. The average proportion of women is 49.9, the average proportion of the poor is 15.9%, and the average of the Social Association Rate is 11.6. We divide the data into four levels according to total cases.
3.2.2 Which race is the majority of the sample?
According to the average value, we get a pie chart of race proportions, from which we can see the overall proportions of different races. In the following, we will study the proportion of which race is related to the number of confirmed cases and the number of deaths.
3.3 Stay at home policy in each province
3 Descriptive Statistics
3.1 Patient Demographics (Impact of Race)
3.2 Underlying Health Conditions
3.3 Impact of Temperature
=======3.5 Impact of Temperature
4 Chapter 4: Independent Variables EDA: Boxplots, Scatterplots, ANOVA, & Chi-Square
[1] 0 2 9 39 110465
Shapiro-Wilk normality test
data: df2$TC
W = 0.05, p-value <0.0000000000000002
Bartlett test of homogeneity of variances
data: TC by rank
Bartlett's K-squared = 25341, df = 3, p-value <0.0000000000000002The Shapiro-Wilk test is used to test whether the data conforms to the normal distribution. H0: The sample data is not significantly different from the normal distribution H1: The sample data is significantly different from the normal distribution The p-value is less than 0.05, the null hypothesis is rejected, and the total cases do not conform to the normal distribution.
Test for homogeneity of variance H0: The variances of the groups are not significantly different H1: The variances of several groups are significantly different The result shows that the p value is less than 0.05, rejecting the null hypothesis, and total cases do not meet the homogeneity of variance.
4.1 SMART Question: The proportion of which race is related to the number of confirmed cases and deaths
TC death black AIAN Asian NH Hispanic
TC 1.000000 0.972823 0.0496 -0.01686 0.18599 0.000604 0.06664
death 0.972823 1.000000 0.0393 -0.01172 0.13787 -0.000567 0.04368
black 0.049607 0.039285 1.0000 -0.10864 0.02160 -0.024383 -0.10517
AIAN -0.016857 -0.011717 -0.1086 1.00000 -0.00968 0.004407 0.00514
Asian 0.185989 0.137871 0.0216 -0.00968 1.00000 0.234855 0.15951
NH 0.000604 -0.000567 -0.0244 0.00441 0.23486 1.000000 0.02912
Hispanic 0.066637 0.043680 -0.1052 0.00514 0.15951 0.029123 1.00000
NHW -0.099814 -0.072005 -0.5868 -0.31897 -0.29574 -0.111971 -0.61564
NHW
TC -0.0998
death -0.0720
black -0.5868
AIAN -0.3190
Asian -0.2957
NH -0.1120
Hispanic -0.6156
NHW 1.0000
First, let’s look at the relationship between the total number of cases and race. It can be seen from the correlation coefficient graph that the positive correlation coefficient between total cases and Asians is the largest, which indicates that the higher the proportion of Asians, the more total cases, and Asian infections may be more serious. The other two races with higher correlation coefficients with TC are Hispanics and blacks. The correlation coefficient between total cases and non-Hispanic whites is negative, indicating that the higher the proportion of this race, the fewer the number of people diagnosed with COVID-19.
Second, we can see the relationship between the number of deaths and race, and the results are consistent with the above results.
3.4 Social Determinants of Health???